| /* |
| * Licensed to the Apache Software Foundation (ASF) under one or more |
| * contributor license agreements. See the NOTICE file distributed with |
| * this work for additional information regarding copyright ownership. |
| * The ASF licenses this file to You under the Apache License, Version 2.0 |
| * (the "License"); you may not use this file except in compliance with |
| * the License. You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| */ |
| |
| package org.apache.commons.math4.ml.neuralnet; |
| |
| import java.util.Set; |
| import java.util.HashSet; |
| |
| import org.apache.commons.math4.ml.distance.DistanceMeasure; |
| import org.apache.commons.math4.ml.distance.EuclideanDistance; |
| import org.apache.commons.math4.ml.neuralnet.FeatureInitializer; |
| import org.apache.commons.math4.ml.neuralnet.FeatureInitializerFactory; |
| import org.apache.commons.math4.ml.neuralnet.MapUtils; |
| import org.apache.commons.math4.ml.neuralnet.Network; |
| import org.apache.commons.math4.ml.neuralnet.Neuron; |
| import org.apache.commons.math4.ml.neuralnet.oned.NeuronString; |
| import org.junit.Test; |
| import org.junit.Assert; |
| |
| /** |
| * Tests for {@link MapUtils} class. |
| */ |
| public class MapUtilsTest { |
| /* |
| * Test assumes that the network is |
| * |
| * 0-----1-----2 |
| */ |
| @Test |
| public void testFindClosestNeuron() { |
| final FeatureInitializer init |
| = new OffsetFeatureInitializer(FeatureInitializerFactory.uniform(-0.1, 0.1)); |
| final FeatureInitializer[] initArray = { init }; |
| |
| final Network net = new NeuronString(3, false, initArray).getNetwork(); |
| final DistanceMeasure dist = new EuclideanDistance(); |
| |
| final Set<Neuron> allBest = new HashSet<>(); |
| final Set<Neuron> best = new HashSet<>(); |
| double[][] features; |
| |
| // The following tests ensures that |
| // 1. the same neuron is always selected when the input feature is |
| // in the range of the initializer, |
| // 2. different network's neuron have been selected by inputs features |
| // that belong to different ranges. |
| |
| best.clear(); |
| features = new double[][] { |
| { -1 }, |
| { 0.4 }, |
| }; |
| for (double[] f : features) { |
| best.add(MapUtils.findBest(f, net, dist)); |
| } |
| Assert.assertEquals(1, best.size()); |
| allBest.addAll(best); |
| |
| best.clear(); |
| features = new double[][] { |
| { 0.6 }, |
| { 1.4 }, |
| }; |
| for (double[] f : features) { |
| best.add(MapUtils.findBest(f, net, dist)); |
| } |
| Assert.assertEquals(1, best.size()); |
| allBest.addAll(best); |
| |
| best.clear(); |
| features = new double[][] { |
| { 1.6 }, |
| { 3 }, |
| }; |
| for (double[] f : features) { |
| best.add(MapUtils.findBest(f, net, dist)); |
| } |
| Assert.assertEquals(1, best.size()); |
| allBest.addAll(best); |
| |
| Assert.assertEquals(3, allBest.size()); |
| } |
| |
| @Test |
| public void testSort() { |
| final Set<Neuron> list = new HashSet<>(); |
| |
| for (int i = 0; i < 4; i++) { |
| list.add(new Neuron(i, new double[] { i - 0.5 })); |
| } |
| |
| final Neuron[] sorted = MapUtils.sort(new double[] { 3.4 }, |
| list, |
| new EuclideanDistance()); |
| |
| final long[] expected = new long[] { 3, 2, 1, 0 }; |
| for (int i = 0; i < list.size(); i++) { |
| Assert.assertEquals(expected[i], sorted[i].getIdentifier()); |
| } |
| } |
| } |